In a digital economy, success only can only be achieved by establishing a product-market fit. As a starter, this means collecting customer data and acting upon it. Competitive edge boils down to brand assets, customer relationships and the business's ability to innovate in the right direction. For most companies, that starts with a data strategy, and ends with the execution of smarter decisions. Whether that be within in the planning of advertising, messaging, product development or customer engagement, the same rules apply. Know more about your current and potential customers than the competition.

So, I know everything about you...

Into this breach stepped data enrichment organisations, whom throughout a magical decade, between the mainstreaming of big data and the introduction of GDPR, had a field day. They harnessed publicly available data (or otherwise), scraped social media; merged massive datasets at whim. Learning more about those on your CRM was an obvious win. So, if your customer didn't tell you their marital status, you just need to merge in the voter registry to have a fairly good idea. And so, the name of the game was to API into everything. The best data integration tools would also tap into social media profiles with beautifully animated zooming visualisations.

I remember a meeting with such a data integration company, circa 2008. The sales rep stood at the head of the table in a awfully tiny meeting room. He opened his Dell ThinkPad, looked straight ahead to the opposite side of the table where my senior colleague sat. He pointed, with a gesture which did not politely match the dimensions of the room. ThinkPad Man then began rattling off everything from the genre of TV my colleague's family mostly watched, voter attendance, gym membership and household composition, including the names of his children. It was superbly impressive and sucked up all the oxygen in the room.

We did not partner with this data service - because, despite being absolutely legal, it was still creepy. That was then. This is now. Now, it's still super creepy and absolutely illegal.

It isn't about compliance.

GDPR means that you cannot merge data you have on your customer with data from elsewhere without positive opt in. That is a major hurdle to data enrichment. Many organisations cite "legitmate business reasons" knowing that if tested, or subject to a single complaint would scupper the entire business. My and your information is of course none of their business. Consent is everything. Further, an organisation is also jointly responsible for the data they have. This means there is no turning a blind eye to a service which merges data without positive consent. A claim that opt-ins have occurred, or that data privacy has been respected does not absolve the buyer. The information officer (ICO) is handing out fines.

The consequences of creepy with data breaks two long held marketing wisdoms - the first is that this is an exception to the "There's no such thing as bad PR" rule" - wanna bet? When your customers even suspect you of being creepy with data they simply won't touch you again. One could go so far as to suggest that if a mid-sized organisation was creepy with their data, they wouldn't survive the year.

That's because it is not really a data privacy legislation issue. It never was. Remember the Cambridge Analytica scandal? It was about merging social media profiles with survey data. The public are now primed to it, and less effected by the legality. They simply do not like you knowing more about them than they want you to know - chiefly because no one likes being manipulated. This is also the one betrayal that breaks another marketing rule - the 6-month sunset. (Most customer's will forgive you for anything after 6 months once the emotion fades and you are still offering the same value proposition). The reason they don't here, is because they would feel vulnerable doing so - not entirely an unemotional association. Offering good value does not balance out being made to feel exposed, naked; a salesperson's sitting-duck.

The rules of competitive advantage through data, however, have not changed. Marketing's mission is still to do the most with the information you have about your customer and infer as much as you plausibly can. This the wheelhouse of Predictive Analytics where even the mildest versions of Machine Learning can infer quite a bit with reasonably okay accuracy. It's common place to move a customer segmentation variable from one dataset to the next, should there be enough relevant overlapping information.

The ethics of using predictive analytics on customer data.

Predictiveness analytics however doesn't suffer from same nefarious stigmatism, for it is using the data customers gave you in a smart way, and not attempting to supplement it with a customer's data found elsewhere. Moreover, it's not personal, in the way a recommendation engine is not personal: "Here is what you might like based on what other people have said". In the world of data integration "Here is what you might be like, based on what other people have said. I don't know, I'm just hazarding a guess." When a company does this, and gets it correct, customers tend to perceive it as a positive thing - merely something a clever marketer from a clever company, deserving of their money, would do.

Even within the best data integration tools, predictive analytics inherently suffers from the problem of repeated estimation. Making one prediction about an individual correctly, does not mean that its next prediction will be cogent with the first. As one makes a third and forth prediction, the data becomes a mess.

Data Fusion tools, in contrast, do not suffer from this problem, nor do they draw in or generate synthetic data (although the output is arguably synthetic, only in that it is synthetically single source.) No Generative AI is needed - the data is perfectly internally coherant because we're not estimating and guessing variables, or manufacturing new data - we're matching real people with real people.

But like standard machine learning Data Fusion does not pretend to match an individual correctly or use that individual's own data. In fact, it guarantees (pretty much) that each prediction will be incorrect. And so, Data Fusion tools can be used to target customers, and marketing efforts are received positively without the creepiness factor. In fact, used strategically, it has the potential to tell marketers far more than any data enrichment company could ever dream to.



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How fusion approaches data integration problems differently and achieves perfect consistency amongst mapped variables.

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